This document describes the various replication files associated with
"Bootstrap Confidence Regions for Multidimensional Scaling Solutions." 


  I. FILES USED TO CREATE THE INPUT DATA FOR ANALYSES REPORTED IN THE PAPER

     "create feeling therm dataset.sas"

        This file contains the SAS session that extracts the feeling 
        thermometer data from the 2004 ANES, and deletes observations
        with missing values on any of the thermometers. The result is
        the 711 by 13 matrix of thermometer scores used to create the
        LOS dissimilarities matrix. This is also the data matrix that is
        resampled in the bootstrapping application. The data are written 
        to the SAS dataset, "therms.sas7bdat".


     "create candidate eval data.sas"

        This file contains the SAS session that uses the 2004 ANES 
        to create a dataset in which each observation is an individual
        respondents evaluation of a single political figures. There 
        are 13 political figures, so the total number of observations
        in the new dataset is 13 times the number of survey respondents
        in the ANES. These data are passed to R, and used to estimate the
        multilevel model of candidate evaluation summarized in Table 2, 
        and discussed in greater detail in the supplemental report, 
        "A Multilevel Model Showing the Effects of Uncertainty on 
        Individual Candidate Evaluations." The data are written to the 
        SAS dataset, "ceval.sas7bdat".


     "nes04dat.txt"

        Data file for 2004 ANES. This is the April 18, 2005 release of 
        the data, which is used throughout this analysis.


     "therms.sas7bdat"

        SAS dataset containing 711 respondents' feeling thermometer
        ratings of 13 political figures. Used to create the substantive
        application of MDS and to perform the substantive application
        of the bootstrapping procedure.


     "ceval.sas7bdat"

        SAS dataset in which each observation is an individual 
        respondents' evaluation of a single political figures. 
        There are 13 political figures, so the total number of 
        observations in the new dataset is 13 times the number of 
        survey respondents in the ANES. These data are passed to R, 
        and used to estimate the multilevel model of candidate 
        evaluation summarized in Table 2, and discussed in greater 
        detail in the supplemental report, "A Multilevel Model Showing 
        the Effects of Uncertainty on Individual Candidate Evaluations."
        The variables in this dataset are as follows:

           caseid   Case ID number for NES respondent

            cname   Name of the candidate being evaluated


             eval  Feeling thermometer score, on a -50 to 50 scale

             dist  Distance between candidate's position on horizontal MDS 
                   axis (which has been transformed to range from 0 through 
                   6) and respondent's liberal-conservative 
                   score (0-6 scale).

            width  Width of candidate's 95% confidence ellipse along the 
                   horizontal axis (again, transformed to correspond to 
                   the 0-6 scale used for the candidates)

          interac  Multiplicative term between "dist" and "width"

            copar  Dummy variable coded 1 if respondent and candidate 
                   are same party, zero otherwise. Nonleaning independents 
                   are treated as copartisans of Ralph Nader.


     "ceval.dta"

        Stata dataset containing exactly the same data as the SAS
        SAS dataset, "ceval.sas7bdat". This file was created using
        StatTransfer 11.



 II. FILES USED TO CARRY OUT MDS APPLICATION

     "create los dissims matrix.sas"

        This file contains the SAS session that calculates the 
        line-of-sight (LOS) dissimilarities matrix, to be used
        as input to the basic multidimensional scaling analysis
        used in the substantive application. This SAS session
        uses the SAS dataset contained in the file, "terms.sas7bdat".


     "basic mds analysis.sas"

        This file contains the SAS session that uses the LOS
        dissimilarities matrix obtained as output from the 
        SAS session in "create los dissims matrix.sas" to
        carry out a classical multidimensional scaling analysis
        of perceptual dissimilarities between 13 political
        figures from the 2004 presidential election. The LOS
        matrix is included within this file (it is not read
        from an external file).

   
     "mds config, with names.txt"

        This file contains the coordinates for the two-dimensional
        MDS solution obtained from the SAS session contained in
        the file, "basic mds analysis.sas". The file contains
        a header record giving variable names.


     "create figure 1.R"

        This file contains the R script to create Figure 1. The
        R session requires the point configuration contained in
        the file, "mds config, with names.txt".



III. FILES USED FOR APPLICATION OF BOOTSTRAPPING PROCEDURE

     "bootstrap mds application.sas"

        This file contains the SAS session that carries out the 
        bootstrapping procedure on the 2004 data. It begins the
        analysis with the LOS dissimilarities created in the SAS
        session contained in "create los dissims matrix.sas". It
        then uses the data in "therms.sas7bdat" to create 50
        bootstrap replications of the feeling thermometer data, 
        calculate the LOS dissimilarities for each one, and 
        replicate the MDS on the bootstrapped LOS matrices.
        Finally, the bootstrap replications of the MDS are 
        brought into maximal geometric similarity to the original
        MDS solution.


     "output from bootstrap MDS.txt"

        This file contains the printed output from the SAS 
        session contained in "bootstrap mds application.sas".


     "bootstrap estims, with names.txt"

        This file contains the 50 bootstrap replications of
        the point coordinates for the 13 political figures 
        used in the substantive application of the bootstrap
        MDS procedure. The file contains a header record giving
        variable names.


     "create figure 2.R"

        This file contains the R script to create Figure 2. The
        R session requires the point configuration contained in
        the file, "mds config, with names.txt" and the 50
        bootstrap replications contained in the file, "bootstrap
        estims, with names.txt".



 IV. FILE USED TO CREATE FIGURE IN SUPPLEMENTAL REPORT ON 
     ASSESSING MULTIVARIATE NORMALITY OF BOOTSTRAP REPLICATIONS

     "create chi-square qq plot.R"

        This file contains the R script to create Figure 1 in the
        supplemental report, "Assessing Multivariate Normality in
        Bootstrap Replications of the MDS Point Coordinates." The
        R session requires the 50 bootstrap replications of the 
        MDS solution, contained in "bootstrap estims, with names.txt".


  V. R SCRIPT FOR MULTILEVEL MODEL OF CANDIDATE EVALUATIONS

     "estimate multilevel model of candidate eval.R"

        This file contains the R script to estimate the
        multilevel model of candidate evalutions. The model
        specification is given in equation (1) in "Bootstrap
        Confidence Regions for Multidimensional Scaling
        Solutions" and the coefficient estimates are
        given in Table 2. The model is discussed in greater
        detail in the supplemental report, "A Multilevel
        Model Showing the Effects of Uncertainty on
        Individual Candidate Evaluations." This file also contains
        the R code to reproduce Figure 1 in that report. Note that the
        analysis contained in this file requires the Stata dataset
        contained in the file, "ceval.dta".


  VI. R PACKAGE FOR BOOTSTRAP MDS CONFIDENCE REGIONS

     "bsmds_01.1.tar.gz"

        This file contains the source code for the R package, 
        "bsmds", which carries out an MDS analysis and produces
        bootstrap confidence regions for the point configuration,
        as explained in "Bootstrap Confidence Regions for
        Multidimensional Scaling Solutions." To install, copy
        the file to R's working directory, and enter the following:

          install.packages("bsmds_0.1b.tar.gz", type="source", repos=NULL)
